We explore expanding the models with additional LM data, as this has been proven effective for improving hybrid model performance on an unseen domain.
Table REF  shows the number of words provided by each language model data source, including the supervised acoustic transcripts as well as the unsupervised pseudotranscripts (which are combined with the supervised acoustic transcripts in training baseline semisupervised LMs).
Set 1 contains a small amount of parallel data from the MATERIAL program used in training a machine translation system.
This demonstrates the benefits that even a small amount of additional data can have in adapting the domain.
We have scraped a large amount of text data from the web, as described in [1]}, and this data is used in Set 2.
In addition, for Bulgarian and Lithuanian, we include additional data from the Paracrawl corpus [2]} in Set 2.
Using this set in language model training shows the improvements that can be obtained from a much larger—though less refined—corpus of language model data.
